.png){kind=icon}
返回
Link Prediction Based on Stochastic Information Diffusion
.png){kind=icon}
PRE
AI
DOI：10.1109/TNNLS.2021.3053263.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Link prediction (LP) in networks aims at determining future interactions among elements; it is a critical machine-learning tool in different domains, ranging from genomics to social networks to marketing, especially in e-commerce recommender systems. Although many LP techniques have been developed in the prior art, most of them consider only static structures of the underlying networks, rarely incorporating the network's information flow. Exploiting the impact of dynamic streams, such as information diffusion, is still an open research topic for LP. Information diffusion allows nodes to receive information beyond their social circles, which, in turn, can influence the creation of new links. In this work, we analyze the LP effects through two diffusion approaches, susceptible-infected-recovered and independent cascade. As a result, we propose the progressive-diffusion (PD) method for LP based on nodes' propagation dynamics. The proposed model leverages a stochastic discrete-time rumor model centered on each node's propagation dynamics. It presents low-memory and low-processing footprints and is amenable to parallel and distributed processing implementation. Finally, we also introduce an evaluation metric for LP methods considering both the information diffusion capacity and the LP accuracy. Experimental results on a series of benchmarks attest to the proposed method's effectiveness compared with the prior art in both criteria.
Keyword:
Task analysis
Stochastic processes
Social networking (online)
Symmetric matrices
Art
Data models
Epidemics
Diffusion process
edge additions
graph based
information spreading
link prediction (LP)
network evolution
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8.9
论文数:
7.6K
被引数:
7.2W
机构
引用论文
.png){kind=icon}
分享收藏分享收藏
分享收藏Effects of rewiring strategies on information spreading in complex dynamic networks重组策略对复杂动态网络中信息传播的影响
PREAI
分享收藏
查看更多内容